The purpose of this research is to develop a comprehensive model of the perception of 3D object motion. The recovery of object motion in a 3D environment is important for perceptual tasks such as the detection and avoidance of collisions and performing interceptive actions. The recovery of object motion is also important for cognitive tasks such as planning future events and navigation. Successful performance of these tasks can be important for the health and safety of the observer. For example, successfully detecting and avoiding collisions is an issue in driving safety and accident risk. In addition, a decreased ability to detect and avoid collisions has been shown to be an important factor in the increased risk of falls among the elderly. Understanding the processes involved in the perception of 3D object motion is an important and necessary step in addressing these health issues. We propose that the recovery of 3D object motion is determined by analyses of optical motion, binocular information, and scene information. A series of experiments will be conducted to determine how observers recover the path and speed of an object moving through a 3D scene from these analyses. The types of information to be investigated are: (a) optical motion-the rate of change in projected size and projected position, (b) binocular information, including disparity and vergence, and (c) scene information, including changes in the projected position of an object along a ground plane. In some experiments, noise will be added to one or more sources of information. Observer tasks will include discrimination between different orientations of straight and curved paths, discriminating between straight paths and paths that are bent or curved, and estimating the future position of a moving object. Other experiments will examine the metrics used by an observer in recovering 3D object motion, such as object size, interocular separation, eyeheight, and stride length. The results of these experiments will be used to develop and validate a general model of 3D object motion based on the intrinsic constraints (1C) model. The general model will integrate information from optical motion, binocular, and scene based analyses to estimate the accuracy and bias of human observers in judging the trajectory of objects moving in the 3D world.